We primarily focus on detecting whether a tweet contains offensive content or not (subtask A), and then determining the target of the offensive content (subtask C). For subtask A, we use pre-trained word embeddings by fine-tuning the BERT model [1]} for detecting offensive tweets. For subtasks B and C, BERT did not perform well, either because of limited training data or because we did not find the appropriate hyperparameters. Thus we use an SVM classifier with character \(n\) -grams as features. We accidentally flipped the predicted labels in our submission to subtask B, which is why we do not report results of subtask B here.
Among all teams participating in OffensEval, our models ranks 3rd out of 103 on subtask A and 27th out of 65 on subtask C. [2]}.
